Leo Schwinn

We propose the first non-autoregressive generative model for relational databases based on a graph view of the database, and we achieve state-of-the-art results on real-world RDBs.

We propose a training-free visual cropping method that leverages MLLM-internal representations for VQA tasks focusing on small details, achieving strong performance with significantly higher efficiency than prior methods.